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Mig-6 suppresses endometrial cancer associated with Pten deficiency and ERK activation.

Cancer research | 2014

PTEN mutations are the most common genetic alterations in endometrial cancer. Loss of PTEN and subsequent AKT activation stimulate estrogen receptor α-dependent pathways that play an important role in endometrial tumorigenesis. The major pathologic phenomenon of endometrial cancer is the loss of ovarian steroid hormone control over uterine epithelial cell proliferation and apoptosis. However, the precise mechanism of PTEN/AKT signaling in endometrial cancer remains poorly understood. The progesterone signaling mediator MIG-6 suppresses estrogen signaling and it has been implicated previously as a tumor suppressor in endometrial cancer. In this study, we show that MIG-6 also acts as a tumor suppressor in endometrial cancers associated with PTEN deficiency. Transgenic mice, where Mig-6 was overexpressed in progesterone receptor-expressing cells, exhibited a relative reduction in uterine tumorigenesis caused by Pten deficiency. ERK1/2 was phosphorylated in uterine tumors and administration of an ERK1/2 inhibitor suppressed cancer progression in PR(cre/+)Pten(f/f) mice. In clinical specimens of endometrial cancer, MIG-6 expression correlated inversely with ERK1/2 phosphorylation during progression. Taken together, our findings suggest that Mig-6 regulates ERK1/2 phosphorylation and that it is crucial for progression of PTEN-mutant endometrial cancers, providing a mechanistic rationale for the evaluation of ERK1/2 inhibitors as a therapeutic treatment in human endometrial cancer.

Pubmed ID: 25377472 RIS Download

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Associated grants

  • Agency: NCI NIH HHS, United States
    Id: P50 CA083639
  • Agency: NICHD NIH HHS, United States
    Id: R01 HD057873
  • Agency: NICHD NIH HHS, United States
    Id: 5R01HD057873

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Gene Expression Omnibus (GEO) (tool)

RRID:SCR_005012

Functional genomics data repository supporting MIAME-compliant data submissions. Includes microarray-based experiments measuring the abundance of mRNA, genomic DNA, and protein molecules, as well as non-array-based technologies such as serial analysis of gene expression (SAGE) and mass spectrometry proteomic technology. Array- and sequence-based data are accepted. Collection of curated gene expression DataSets, as well as original Series and Platform records. The database can be searched using keywords, organism, DataSet type and authors. DataSet records contain additional resources including cluster tools and differential expression queries.

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